Mining frequent itemsets from secondary memory

被引：22

作者：

Grahne, G ^{[1
]}

Zhu, JF ^{[1
]}

机构：

[1] Concordia Univ, Montreal, PQ, Canada

来源：

FOURTH IEEE INTERNATIONAL CONFERENCE ON DATA MINING, PROCEEDINGS | 2004年

关键词：

D O I：

10.1109/ICDM.2004.10116

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Mining frequent itemsets is at the core of mining association rules, and is by now quite well understood algorithmically for main memory databases. In this paper we investigate approaches to mining frequent itemsets when the database or the data structures used in the mining are too large to fit in main memory. Experimental results show that our techniques reduce the required disk accesses by orders of magnitude, and enable truly scalable data mining.

引用

页码：91 / 98

页数：8

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